Structural ordering of coal char during heat treatment and its impact on reactivity

Carbon 40 (2002) 481–496

Structural ordering of coal char during heat treatment and its impact on reactivity Bo Feng a , Suresh K. Bhatia a , *, John C. Barry b a

b

Department of Chemical Engineering, The University of Queensland, Brisbane, Queensland 4072, Australia Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, Queensland 4072, Australia Received 2 October 2000; accepted 10 April 2001

Abstract The effect of heat treatment on the structure of an Australian semi-anthracite char was studied in detail in the 850–11508C temperature range using XRD, HRTEM, and electrical resistivity techniques. It was found that the carbon crystallite size in the char does not change significantly during heat treatment in the temperature range studied, for both the raw coal and its ash-free derivative obtained by acid treatment. However, the fraction of the organized carbon in the raw coal chars, determined by XRD, increased with increase of heat treatment time and temperature, while that for the ash-free coal chars remained almost unchanged. This suggests the occurrence of catalytic ordering during heat treatment, supported by the observation that the electrical resistivity of the raw coal chars decreased with heat treatment, while that of the ash-free coal chars did not vary significantly. Further confirmatory evidence was provided by high resolution transmission electron micrographs depicting well-organized carbon layers surrounding iron particles. It is also found that the fraction of organized carbon does not reach unity, but attains an apparent equilibrium value that increases with increase in temperature, providing an apparent heat of ordering of 71.7 kJ mol 21 in the temperature range studied. Good temperature-independent correlation was found between the electrical resistivity and the organized carbon fraction, indicating that electrical resistivity is indeed structure sensitive. Good correlation was also found between the electrical resistivity and the reactivity of coal char. All these results strongly suggest that the thermal deactivation is the result of a crystallite-perfecting process, which is effectively catalyzed by the inorganic matter in the coal char. Based on kinetic interpretation of the data it is concluded that the process is diffusion controlled, most likely involving transport of iron in the inter-crystallite nanospaces in the temperature range studied. The activation energy of this transport process is found to be very low, at about 11.8 kJ mol 21 , which is corroborated by model-free correlation of the temporal variation of organized carbon fraction as well as electrical resistivity data using the superposition method, and is suggestive of surface transport of iron.  2002 Elsevier Science Ltd. All rights reserved. Keywords: A. Char; B. Heat treatment; D. Transmission electron microscopy, X-ray diffraction; D. Reactivity

1. Introduction Thermal deactivation, i.e. decrease of gasification reactivity of carbon or coal char after heat treatment at high temperatures, has been reported by many investigators [1–6]. Hurt and Gibbins [7] proposed that thermal deactivation or thermal annealing leads to the much lower reactivity of the residual carbon during the late stage of combustion in pulverized coal fired boilers. Senneca et al. *Corresponding author. Tel.: 161-7-3365-4263; fax: 161-73365-4199. E-mail address: [email protected] (S.K. Bhatia).

[8] also found that the time scales of annealing and of CO 2 gasification are close to each other, indicating that the overall CO 2 gasification rate could be decreased significantly by thermal annealing. Other investigations support the view that the reactivity of coal char may be decreased due to thermal deactivation in combustion systems [9,10]. These results suggest the need of incorporation of thermal deactivation kinetics in gasification modelling. It is believed that thermal deactivation is at least partly due to the structural change during heat treatment, which results in decrease in the number of active sites for reaction [2,11,12]. Therefore the establishment of an accurate thermal deactivation model relies on the understanding of

0008-6223 / 02 / $ – see front matter  2002 Elsevier Science Ltd. All rights reserved. PII: S0008-6223( 01 )00137-3

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the structure variation during heat treatment, which is still incomplete. The structural variation of carbon during heat treatment has been studied by different workers with the help of numerous techniques, such as high resolution transmission electron microscopy (HRTEM), X-ray diffraction (XRD) and Raman spectroscopy. Comprehensive HRTEM studies of carbon structure during heat treatment have been done by Oberlin and coworkers [13–16], demonstrating four stages for the change of the micro-texture during carbonization up to 30008C [15]: Stage 1 (,7008C): This corresponds to the release of the aromatic CH groups. Each fragment obtained by grinding is a lamella where the aromatic layers are approximately parallel to the lamella plane. The individual scattering domains [basic structural unit (BSU)] are less than 1 nm in diameter and are isometric. Their thickness is also about 1 nm (N, the number of layers, varies from 1 to 3). The BSUs are azimuthally distributed at random in the fragments. Stage 2 (700–15008C): This corresponds to the association of BSUs face to face, i.e. to the release of interlayer defects between two superimposed BSUs. The lamella shape is more pronounced and the lamella are thin and flexible enough to be folded. N jumps from stage 1 to stage 2 and reaches 8–10. Stage 3 (1500–20008C): This corresponds to the release of in-plane defects. The disappearance of the misoriented single BSU permits first a considerable increase in thickness, and then the coalescence of adjacent columns. The distortions of the layers progressively anneal. During this stage, the layers inside the columns associate edge to edge. Stage 4 (.20008C): Above 20008C, all distortions are annealed. The layers are stiff and perfect. The heteroatoms and both the interlayer and in-plane defects have successively disappeared. Crystal growth may thus begin. Emmerich [17] studied the variation of the dimensions La (diameter) and Lc (height) of the graphite-like crystallites of graphitizable and non-graphitizable carbons with heat treatment using XRD techniques. He found that the increase of La and Lc with heat treatment temperature could be attributed to one or more of three processes — in-plane crystallite growth, coalescence of crystallites along the c-axis and coalescence of crystallites along the a-axis — depending on the temperature range. He observed that the temperature separating the vegetative increase and the coalescent increase of La and Lc depends largely on the carbon used. For the graphitizable 3.5DMPF resin char, the coalescent increase dominates after 13508C, while for the non-graphitable P-F resin char the vegetative increase dominates until 25008C. Using Raman spectroscopy, Grumer et al. [18] also found that above a

certain temperature La and Lc increase sharply with increase of heat treatment temperature, consistent with the XRD values. There are some early investigations on the kinetics of graphitization of carbon, as reviewed by Fischbach [19]and Pacault [20], but mostly in the temperature range of stage 4. The kinetics was studied by using a number of structuredependent physical properties, such as diamagnetic susceptibility, thermal and electrical conductivities, Hall coefficient, magnetoresistance, thermoelectric power and electron paramagnetic resonance behavior. Two regimes were reported for many carbons: regime I is that in which layer ordering is the dominant process, while regime II is one in which increasing La plays a very important role. The effective activation energy for the graphitization process was found to be in a range of 90–270 kcal mol 21 in the high temperature range of stage 4. Actual gasification systems generally operate in the temperature range of stage 2, although some coal combustion furnaces run at temperatures as high as 17008C. However, so far there have been very few studies on the kinetics of graphitization in this lower temperature range (that of stage 2). Suuberg et al. [11] have made some efforts in this direction by studying the low temperature chemisorption capacity of carbon during heat treatment, i.e. the active surface area, which is structure sensitive. They considered the heat annealing process to involve multiple reactions with a distribution of activation energy, with each being a simple first order reaction. Other studies assumed that the reactivity is influenced exclusively by the structural reordering, and obtained apparent kinetic parameters directly from the reactivity data. The gasification reaction scheme was simplified into several parallel reactions: an annealing reaction that converts the more reactive carbon into a less reactive form, and the reactions of the two carbons with the gasifying agent. This is the Nagle–Strickland–Constable model [21], utilized by Senneca et al. [8]. They found that annealing and gasification occur over comparable time scales under the experimental conditions investigated, and this feature largely controls the evolution of char reactivity throughout burnoff. In the present work we have studied the structural variation of an Australian coal during heat treatment in the temperature range of stage 2 using XRD techniques. The structural parameters were obtained by fitting the XRD patterns using a new XRD structure–refinement program [22], specifically developed for X-ray powder diffraction data collected on disordered carbons. The program minimizes the difference between the observed and calculated diffraction profiles in a least-squares sense by optimizing model parameters, analogous to the popular Rietveld refinement method. Unlike the Rietveld method, which is designed for crystalline materials, this program allows the quantification of the finite size, strain and disorder present in disordered carbon fibers and cokes. For example, the structural model used includes the probability of random

B. Feng et al. / Carbon 40 (2002) 481 – 496

translation parallel to adjacent carbon layers as a refinable parameter describing turbostratic disorder. Other parameters are used to describe finite size, fluctuations in the spacing between adjacent layers, average lattice constants, background and other important quantities. The structural model, combined with the refinement program, acceptably describes the diffraction patterns from disordered carbons such as pitch heated near 823 K, cokes, fibers, heat-treated cokes and synthetic graphite. The present paper also uses the techniques of HRTEM and electrical resistivity for providing supporting evidence, and as additional tools for probing of the structural changes. While HRTEM enables visualization of the structural changes, electrical resistivity provides an additional measure of the extent of reordering since this process leads to conductivity increase in the chars. The variation of reactivity of the coal char with heat treatment was also obtained, and correlation was found between the reactivity and the structural parameters.

2. Experimental

483

Table 1 Properties of Yarrabee coal Net calorific value (MJ kg 21 )

28.40

Proximate analysis (%) Moisture Ash Volatile matter Fixed carbon Volatile matter (%), dry basis

3.0 10.0 9.5 77.5 10.9

Ultimate analysis (%), dry basis Carbon Hydrogen Nitrogen Sulphur Oxygen

92.77 3.54 1.83 1.22 0.64

Relative density Petrographic analysis (%) Vitrinite Liptinite Inertinite Mineral

1.34

39 – 3 5

2.1. Materials and acid-washing An Australian semi-anthracite, Yarrabee, was used, whose physical and chemical properties are listed in Table 1. The coal particles were sieved to within a size range of 90–106 mm, and then subject to acid washing and heat treatment. The raw coal was washed in HCl (50%) at 608C for 24 h, and then in distilled water until no Cl 2 was detected in the waste solution, filtered and dried in air. Around 27% of the ash was removed by this process. The HCl washed coal was further washed in HF (50%) at 608C for 2 h, then filtered and washed again in HCl (50%) at 608C for 2 h. The sample was subsequently washed in distilled water several times until no Cl 2 was detected in the waste solution. Most of the ash was removed by this process. The ash-free coal was also heat-treated.

2.2. Heat treatment The sieved coal sample (termed rawy or HFy) was inserted into a tube furnace kept at 11508C, and maintained under a nitrogen stream (4 l min 21 ) for 2 min. Subsequently it was removed from the furnace and kept at room temperature in N 2 to cool down. The resulting sample, named as rawy2mA or HFy2mA [rawy indicates raw Yarrabee coal, and HFy the HF-washed coal, 2m represents the heating time (2 min), and A stands for the heat treatment temperature (11508C)], was used as the starting material for the subsequent heat treatment. The starting material was then heat-treated at various temperatures (from 850 to 11508C) for various times (from 10 min to 15 h). More than 100 heat-treated samples were generated,

Analysis of ash (%) SiO 2 Al 2 O 3 Fe 2 O 3 TiO 2 Mn 3 O 4 CaO MgO Na 2 O K2O P2 O 5 SO 3 BaO SrO ZnO Ash fusion temperature (8C) Reducing atmosphere Deformation Sphere Hemisphere Flow

52.3 23.7 16.21 0.60 0.23 2.30 1.82 0.82 0.97 0.90 1.08 0.05 0.03 0.01

1200 – 1400 1500

which were named according to the same rules above. ‘A’ to ‘G’ were used to represent the temperatures from 1150 to 8508C, with an interval of 508C. Therefore HFy8hC represents the HFy2mA sample that was further heattreated at 10508C for 8 h.

2.3. Reactivity analysis Reactivity measurement was performed for all the samples on a TGA (Cahn TG-121). The reaction tempera-

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ture was fixed to be 673 K and pure oxygen was used as oxidizer. Low temperature oxidation was used here to ensure no further heat annealing occurring during gasification. Preliminary runs with different particle sizes were performed to ensure that the reaction is under chemical control. For each run around 5 mg sample of particle size of 90–106 mm was used. The furnace was heated at a rate of 20 K min 21 in argon until 4008C and held at this temperature for 30 min before the gas stream was switched from argon to oxygen. The gas flow rate was controlled at 40 ml min 21 . The reaction rate reported is defined as the reaction rate per gram of combustible carbon. For convenience the reaction rate at 1% conversion was used as the initial rate, since the latter at t50 is difficult to measure accurately.

2.4. XRD analysis Several coal char samples were subjected to XRD analysis. The powder diffractometer (Philips PW 1710), ˚ fitted with a copper radiation source ( l1 51.54060 A, ˚ was configured in the Bragg–Brentano l2 51.54439 A), pseudo-focusing geometry. Measurements were recorded from a start angle 2u 5108 to an end angle of 1008 with a scanning speed of 0.58 min 21 . The XRD patterns were analyzed for the structural parameters using two methods. One is the classical Scherrer equation, and the other is the structure refinement technique of Shi et al. [22]. The patterns were also examined using a package, mpsdm, to search for the inorganic impurities in the coal chars. The concentrations of the impurities were estimated from the matching probabilities.

2.5. HRTEM The samples were prepared for electron microscopy by crushing under ethanol in a mortar and pestle. A suspension of the crushed crystal was then deposited onto holey carbon film. The high-resolution transmission electron microscope (HRTEM) images were obtained using a Jeol 2010 transmission electron microscope (Cs51.4 mm; structure resolution limit50.25 nm, information limit5 0.19 nm), which was operated at 200 kV. The elemental compositions of the samples were analysed using an Oxford-Link silicon-crystal energy dispersive X-ray spectrometer (EDS) which is attached to the 2010 electron microscope. The HRTEM images were captured onto Kodak SO-163 electron image film, and these images were subsequently digitised using a Leafscan 45 negative scanner.

2.6. Helium density and pore structure Helium density was measured for several samples using a Micromeritics AccuPyn 1330 helium pycnometer. The

surface area and pore size distribution were obtained by argon adsorption at 87 K using a Micromeritics micropore analyser (ASAP2010). Data analysis was performed using density function theory (DFT), implemented in the Micromeritics DFT software package.

2.7. Electrical resistivity Electrical resistance was measured for all the chars using an ohmmeter (Hioki 3220 m HiTester, range 20 m to 20 K, accuracy 0.2%). The apparatus for determining the resistance of the powdered chars consists of a hollow cylinder constructed with a non-conducting material. Two copper pistons in the both ends of the cell are used to press the samples in the cell and to connect to the ohmmeter. The cell has an inner diameter of 5 mm and a length of 23 mm, with two holes on the side for the detecting probes. The electrical resistance between the two probes was measured and converted into the electrical resistivity r using r 5RA /L, where R is the resistance, A is the area of the cross section of the cell and L is the distance between the two probes (14 mm). For a given sample the measurement was repeated several times after repacking the cell, and the average value was used. Since the compression force on the piston was kept constant, any variation between repeat measurements was due to changes in packing and was generally within 10%. More details can be found in Feng and Bhatia [23].

3. Results and discussion

3.1. Variation of crystallite structure with heating time determined by XRD analysis The XRD patterns of the coal chars were analyzed to obtain their structure parameters. Two methods were used in the present paper to extract the structural information. The first is based on the conventional Scherrer equations which yield the lateral size of the crystallite La , and the stacking height of the crystallite, Lc . However, it is wellknown that the widely used Rietveld analysis has limitations for simulating the XRD pattern of partially graphitized carbons because of the inherent disorder in these materials [24,25]. In particular, a carbon heat-treated at low or moderate temperatures (,18008C) is generally very disordered. This disorder can occur due to a variety of reasons, including the presence of local 3R stacking, random shifts between adjacent layers, unorganized carbons that are not a part of layer structure, and strain in the layers [25]. Scherrer’s equations can be actually only used with highly graphitized carbons, and are not suitable for disordered carbons [26]. Shi et al. [22,26] developed a model that takes into account the inherent disorder present in graphitic carbons to simulate XRD patterns, based on the work of Franklin [27] and Ruland [28]. Their model

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has been proved to be able to simulate well the XRD patterns of numerous carbons generated in widely changed conditions [25,26]. Shi et al. [26] have considered two models: the one-layer model for pregraphitic carbons, and the two-layer model (in which the two-layer package with AB stacking is the primitive scattering unit) for partially graphitized carbons. Their model not only estimates the values of La and Lc , but also the fraction of ordered carbon, g, as well as the in-plane strain and the interplanar spacing d 002 . The values of Lc and La determined by the Scherrer equations increased only slightly with increase of heating time. With increase of the heat treatment temperature (HTT), the values of La and Lc for the raw coal increased marginally. La increased from 1.5 to 1.7 nm, while Lc increased from 0.7 to 0.89 nm when the HTT increased from 1000 to 11508C. Similar to the values of La and Lc of the raw coal char, those of the ash-free do not change appreciably with heating time, although the absolute values of La and Lc are slightly larger than those for the raw coal char, a feature that will be discussed later. The XRD patterns were also fitted using the single layer model of Shi et al. [22], assuming the probability of finding a random displacement between two planes to be unity for the disordered char. Before the fitting of the XRD patterns of the raw coal chars, the peaks due to the impurities in the coal char were removed so that only the peaks due to carbon were left. The XRD patterns were typical for pre-graphitic carbon, having three peaks at 23, 43.6 and 79.88 separately. The single layer model obtains the structure parameter from the shapes and the heights of the three peaks. The variation of the fitted parameters with heat treatment temperature, using the single layer model, is shown in Fig. 1. La and Lc can be seen to increase slightly through the heat treatment, while the interlayer spacing, d 002 , is essentially unchanged during the heat treatment. The values of La , Lc and d 002 also do not change significantly with the HTT. It is also observed that the values of La , Lc and d 002 for the ash-free coal char are very close to those for the raw coal char. The absolute value of La is around 2 nm, slightly larger than that estimated by Scherrer’s equation. However, the absolute values of Lc from this method are almost five times as large as from Scherrer’s equation. Despite the difference in the absolute values of La and Lc , which will be discussed in detail later, both methods lead to the same conclusion that the crystallite size of carbon does not change significantly with heating time in the temperature range studied. Also the crystallite size for the raw coal chars and the ash-free coal chars appears to be similar. In contrast to the above, it was found that the fraction of organized carbon, g, obtained from the single layer model varies significantly with the heating time and the HTT, as shown in Fig. 2. The value of g for the raw coal char increases with heating time as well as with the HTT. Thus it is evident that the crystallites become more organized,

485

Fig. 1. Structure parameters of the raw Yarrabee coal char and the ash-free coal char after heat treatment at various temperatures for various times, obtained using the Shi et al. [22] structure refinement program. Hexagons represent data for the ash-free coal char heat treated at 11508C for various times. d 002 is the distance between two graphene layers.

although their average size does not change markedly. However, the g value for the ash-free coal char remains almost constant during heat treatment at 11508C for various times. These results strongly suggest that the impurities in the raw coal char enhance the structural ordering during heat treatment, i.e. the ordering is predominantly catalytic.

Fig. 2. Fraction of organized carbon in the raw Yarrabee coal char, and in the ash-free coal char, after heat treatment at various temperatures for various times.

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An interesting aspect of Fig. 2 is the possibility of multiple time scales in the ordering process. In the initial stage there is a relatively rapid increase in the value of g, approaching an apparent saturation value, followed by a much slower increase in a second stage, although this could be a slow approach to an apparent equilibrium. With increase in temperature this saturation value increases, in a manner akin to that of an endothermic reversible process. Since ordering is irreversible, this is suggestive of a twostep process, with the possibility of formation of a metastable state of the organized carbon in the first step, followed by a much slower relaxation to the final stable state in the second step. Alternatively, it may be argued that the disorganized carbon is heterogeneous, with the most refractory sites being activated and ordered at higher temperatures. These aspects will be subsequently explored in conjunction with kinetic modelling of the data. The results by both the Scherrer equation and the XRD theory of Shi et al. [22] show that the crystallite size does not change significantly with the heat treatment temperature and heating time, consistent with other investigations [15,17]. On the other hand the fraction of organized carbon increases with the heat treatment temperature and heating time, suggesting that the structural ordering dominates in this case. This also supports the findings of Oberlin and coworkers [14,15] and Emmerich [17], since the temperature range studied is in stage 2, in which in-plane structure ordering resulting from removal of interplanar defects or the vegetative increase in crystallite size (in-plane crystallite growth) plays an important role. Indeed, the fraction of organized carbon can be correlated with La fairly well but not with Lc , implying that in the temperature range studied (850–11508C), structural ordering results in the growth of the crystallite layer in the a-axis only. It is, however, very likely that at higher temperatures (1150– 17008C), the vegetative increase in Lc will also become important. The absolute value of g for the ash-free coal char heat-treated at 11508C for less than 60 min is higher than that for the raw coal char. This is consistent with the finding from the Scherrer equation that the La and Lc values for the ash-free coal char are larger than those for the raw coal char. This higher value of g for the ash-free char could be due to the increase in ordering because of the removal of the impurity atoms. These atoms tend to distort the crystallite layers by locating themselves in or between the crystallite layers. Therefore it is inappropriate to compare the g value for the ash-free coal char directly with that for the raw coal char.

3.2. Electrical resistivity of coal char during heat treatment The electrical resistivity (ER) of the raw coal char and the ash-free coal char after heat treatment was also measured and the results are shown in Fig. 3. As pointed

Fig. 3. Variation of electrical resistivity with heating time, for the raw Yarrabee coal char and the ash-free coal char, after heat treatment at various temperatures.

out by Feng and Bhatia [23], the ER is a structure sensitive parameter that reflects the internal structure in an average manner. The electrical resistivity of the raw coal char decreases with the heating time and the heat treatment temperature, consistent with literature results [19,20]. This also supports the finding of increased ordering by the XRD studies, since the ordered or graphitic form of carbon is the most conductive form. However it is found that the electrical resistivity of the ash-free coal char does not change significantly, again supporting the XRD results of negligible increase in the ordered fraction for this coal. Thus, the structure reordering is much slower for the acid-treated coal, which is confirmed by both the XRD and ER studies. It is also observed that the resistivity of the ash-free coal chars is smaller than that of the raw coal chars, consistent with the higher fraction of the conducting phase in the ash-free coal chars due to the removal of the non-conducting impurities.

3.3. Transmission electron micrographs of heat-treated coal chars The raw coal char and the ash-free coal char after heat treatment were also observed under a high resolution transmission electron microscope. For each sample, many different areas were observed and the elemental compositions in the areas were identified using the energy dispersive X-ray spectroscopy method. The fringe images of the carbon structure in HFy2mA, HFy8hA, y2mA and y8hA showed clear crystallite and yet disordered structure. No effort was made to obtain the crystallite size from processing of the images, as performed by Shim et al. [29]

B. Feng et al. / Carbon 40 (2002) 481 – 496

and Sharma et al. [30]. However, it was difficult to observe any overall pronounced difference in the crystallite structure. Nevertheless, as shown in Fig. 4, very well organized crystallite structure was found in the vicinity of the carbon / iron interface in y12hA, which can not be found in the ash-free samples. As can be seen in Fig. 4a, many crystallites with long parallel-aligned layers are present although they have different orientations. Near-perfect crystallite structure around the iron particles can be seen in

487

Fig. 4b. The carbon in the interface of carbon / clay was also observed under HRTEM, with no dramatic difference being found. All the results from XRD, electrical resistivity and HRTEM suggest strongly the occurrence of catalytic ordering, in which iron is the major catalyst, during heat treatment of the Yarrabee coal. It has long been established that the rate and degree of ordering can be greatly accelerated [19]. The reported effective catalysts include a number of carbides and carbide-forming metals, such as vanadium, nickel, zirconium, iron, titanium, boron, etc. The influence of iron on the graphitization of hard carbons has been attributed to formation and decomposition of Fe 3 C under certain conditions [31]. Although the other metals in our coal char could also act as catalysts, we are led to attribute the catalytic ordering mainly to iron, because it is one of the major components, as shown in Table 1, and because we observe graphitic structures predominantly around the iron particles under HRTEM. In support of our findings, it may be noted that a somewhat more recent study of graphitization in blast furnaces [32], in which mixtures of carborized coke and iron were heated at 16008C, has shown the formation of graphite crystals around iron globules on the surface of the coke. The visual observations were supported by Raman spectroscopy as well as X-ray diffraction, and showed that the crystallite height Lc increases and the interlayer spacing d 002 decreases for both the catalytically formed graphite and the coke. The authors speculated that the mechanism of the catalytic graphitization involved the dissolution and precipitation mechanism.

3.4. Correlation between reactivity and electrical resistivity

Fig. 4. High resolution transmission electron micrographs of the carbon structure around the iron particles for y12hA. (a) Micrograph showing many randomly oriented crystallites, (b) nearperfect crystallite structure around the iron particles.

Since the electrical resistivity is a structure sensitive parameter, it is instructive to relate it with the fraction of organized carbon in char. Indeed, the electrical resistivity can be correlated very well with the organized carbon fraction g, as shown in Fig. 5. For the raw coal char, this correlation is observed to be temperature independent, supporting a one-to-one correlation. The data points for the ash-free coal char are lower than those for the raw coal char, probably because of the increase in the fraction of conducting phase due to the removal of the impurities. The results also suggest that the electrical resistivity is a useful probe in studies of carbon structure. The reactivity of the raw coal char and the ash-free coal char was also obtained and plotted against the electrical resistivity and g in Fig. 6. Here the relative reactivity is expressed as the ratio of initial rate (i.e. that at 1% conversion) of the char to that for char y2mA. Fairly good correlation is found between the reactivity and the ER as well as that between the reactivity and g. Nevertheless these correlations are temperature dependent. Heat treatment at higher temperatures tends to result in lower

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Fig. 5. Correlation of the electrical resistivity with the fraction of organized carbon, g, for the raw coal char and the ash-free coal char, after heat treatment at various temperatures for various times.

reactivity when the structure parameters are the same. The behavior of the char heat-treated at 11508C is very similar to that of the char heat-treated at 11008C. Transformation of iron into iron silicon has been found to occur more actively at these two temperatures than at the lower temperatures. This suggests that deactivation by heat treatment is at least partly due to the transformation of the active catalyst into a form less effective for carbon conversion. In addition, the deactivation could be due to other effects, such as annealing of edge sites or changes that are not observable by XRD. This aspect will be discussed in detail in a subsequent article. The correlation between the ER and the reactivity suggests that the reactivity of the coal char is significantly decreased by the structure ordering, which is effectively catalyzed by iron. Considering the fact that graphitization can be also enhanced by air atmosphere and CO 2 environment [33,34], the influence of in situ graphitization on the reactivity of carbon has been considerably overlooked in the literature. Therefore, the structure ordering and its effect on reactivity should be taken into account in gasification modelling. It may be noted that somewhat complementary results have been obtained by Livneh et al. [35], who studied the influence of heat treatment on ordering and reactivity, using Raman spectroscopy. They find clear correlation between ordering and reactivity in oxygen as well as carbon dioxide, for heat treatment above 12008C but not below 9008C.

3.5. Kinetics of structure reordering As we have discovered in the above sections, ordering during heat treatment of the Yarrabee coal char appears to

Fig. 6. Correlation of the initial reactivity with (a) the electrical resistivity, and (b) the fraction of organized carbon, for the raw coal char and the ash-free coal char, after heat treatment at various temperatures for various times.

be iron-influenced, and leads to significant decrease in the reactivity of the coal char. Based on the determination of fractional ordered carbon, depicted in Fig. 2, a two-stage process is suggested, with a rapid increase of g to an apparent near-equilibrium value in the first stage, followed by a much slower increase in a second stage. A simple mechanism consistent with these observations is that of reversible attainment of a metastable state ( g * ) followed by slow relaxation to the stable one ( gc ), following: u⇔g * → gc

(1)

If the first step is relatively rapid in comparison to the

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second an equilibrium amount of the metastable state g * will be obtained in the first stage, but this state will slowly relax to gc , leading to a slow increase in the total amount ( g * 1 gc ) in the second stage. If the XRD measurement does not distinguish between these states the measured value of g corresponds to this total, yielding the temporal behavior depicted in Fig. 2. An alternate and more likely explanation is that on cooling g * is converted to the stable state g by a second step. Such metastable states following solid-state transformations are well known to exist, and even a subsequent matrix relaxation reported [36] in association with annealing of oxides following carbonate decomposition reactions. The experimental data also suggests that the saturation or equilibrium amount of g * increases with increase in temperature, so that the first step is endothermic. However, the second step of matrix relaxation may be expected to be exothermic. To test the above ideas, and obtain a kinetic model, the superposition method of Pacault [20] was first utilized, which demonstrated the suitability of the approach. Subsequently rate models were fitted to the data for the temporal variation of g, assuming first order reversible kinetics for the first step and first order irreversible kinetics for the second, or a diffusional kinetics for the formation of the metastable state g * . The possibility of a diffusional kinetics was considered based on the observation that ordering was catalyzed by Fe, and that ordering was observed in the vicinity of the iron particles. Thus, it is possible that ordering is initiated at the iron surface and spreads into the surroundings by a diffusional or diffusionlike process. A third kinetic model, based on an irreversible step: u→g

(2)

with an activation energy distribution was also examined. The results of the kinetic interpretations and comparison of the relative merits of these models is presented in the following three sub-sections.

3.5.1. Superposition method As a first attempt to verify the above general idea the superposition concept utilized by Pacault was applied to the data. According to this concept, if an evolving property Pj (t) (e.g. diamagnetic susceptibility, the true Hall coefficient, the interplanar distance or the average crystallite diameter) passes from a value Pj (0) to Pj (`) at any temperature T, with all factors that condition the evolution being held constant, then the degree of evolution defined by: Pj (t) 2 Pj (0) aPj 5 ]]]] Pj (`) 2 Pj (0)

(3)

follows a characteristic functional form:

aPj (t,T ) 5 wPjsk(T )td

(4)

if the system has only one inherent time scale. Here k(T ) is

489

a temperature-dependent rate constant. Consequently, if during heat treatment at temperature T1 the observed evolution of the property Pj (t) follows:

aPj (t,T 1 ) 5 f (t)

(5)

then at temperature T the evolution should follow:

S D

k(T ) aPj (t,T ) 5 f ]]t k(T 1 )

(6)

In addition the form of this function should be independent of the property Pj . Initially superposition was attempted for the fraction ordered carbon assuming g(`)51. In such a case of course even the g(t) curves should be superimposible, since both g(0) and g(`) are independent of temperature. However, this was unsuccessful, with systematic deviations among the curves. Fig. 7a depicts this result for g(t) demonstrating the systematic variation in slopes of the curves at different temperatures, which could not be eliminated by any scaling of the times. Here the abscissa represents the scaled time, log(Dt ), given by: log(Dt ) 5 log(k(T )t)

(7)

with T 1 59508C, and k(T 1 )51. Any variation in k(T ) only provides a linear translation, and cannot affect the slope. Similar systematic deviations were observed for the electrical resistivity data of Fig. 3, despite the larger scatter of that data. This similarity is to be expected given the temperature-independent correlation between ER and g, as depicted in Fig. 5. The above results strongly suggest the presence of multiple time scales, or of a temperature dependent g(`), representing an apparent saturation value. Accordingly the evolution was then superimposed using an extrapolated value of g(`) obtained as the large time value at each temperature in Fig. 2. Fig. 7b shows the resulting excellent superposition, indicating the success of the approach, with no systematic deviation. Improved superposition was also found for the ER data, though with its associated larger scatter. From these results it would appear that the degree of ordering indeed approaches a temperature-dependent saturation limit with an associated single time scale governing the evolution. Fig. 8 depicts the variation of k(T ) /k(T 1 ) with temperature, obtained by this method, indicating similar values and activation energies for the evolution of fractional ordered carbon and ER, at 12.2 and 13.0 kJ mol 21 , respectively. It may be noted that these values are only approximate, as the superposition was guided by eye-estimation rather than any quantitative criterion. Improved estimates can be obtained by data fitting as discussed below. Nevertheless, these activation energy values are much less than those reported for graphitization, in the range of 188–1125 kJ mol 21 [19]. It has been reported that the apparent activation energy of graphitization decreases with the decrease of the heat treatment temperature [19]. The heat treatment temperature studied in the present work is rather low, in the pre-

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B. Feng et al. / Carbon 40 (2002) 481 – 496

Fig. 8. Kinetic parameters obtained by the superposition method and the diffusion model. EER and Eg are the activation energies obtained from the electrical resistivity data and the g data, respectively, while ED represents the activation energy for the diffusion coefficient. Both ordinates have a logarithmic scale.

transformation of unorganized to organized carbon controls the rate, then this transformation is reversible. Since the final ordered state is irreversibly formed this also leads to the postulation of the metastable intermediate, as in Eq. (1). Alternately, the unorganized carbon may be heterogeneous, with the more stable forms being converted at higher temperatures by an irreversible step. Accordingly reaction rate models were formulated based on these alternatives. In the first case, following the reaction scheme in Eq. (1), and assuming first order reactions the following rate model may be derived:

Fig. 7. Variation of (a) fraction of organized carbon, g, and (b) the degree of evolution of g, with log(Dt ), for the raw coal char after heat treatment at various temperatures for various times. Dt is defined in Eq. (7).

graphitic zone, in which the activation energy is smaller. Also it has been shown that catalytic ordering is predominant. The low activation energies reported here therefore imply a different mechanism and associated kinetics than those considered earlier [19] for higher temperatures.

3.5.2. Reaction rate models The above conclusion that the fraction of organized carbon approaches a temperature-dependent apparent equilibrium value suggests that if the kinetics of the

dg ]c 5 k 3 ( g 2 gc ) dt

(8)

dg ] 5 k 1 (1 2 g) 2 k 2 ( g 2 gc ) dt

(9)

Here g( 5 g * 1 gc ) is the measured fraction of organized carbon, assuming that on cooling the metastable form relaxes to the stable one. Furthermore, k 1 and k 2 are the forward and reverse rate constants, respectively, for the first reversible step, while k 3 is the rate constant for the second step. This model was analytically solved and fitted to the measured g(t) data by nonlinear least squares regression, using the initial conditions g(0) 5 gc (0) 5 go , with go having the measured value of 0.25. Alternately we also considered g(0) 5 go , gc (0) 5 0, implying the initial state of the organized carbon to be the metastable one. A third possibility was also considered with k 3 50, so that only k 1 and k 2 were unknowns in the fitting, with gc (t) 5 go 5constant. All the data at the five temperatures was jointly fitted, assuming Arrhenius temperature dependence of each of the rate constants. Good fit was obtained in all

B. Feng et al. / Carbon 40 (2002) 481 – 496

cases; however, the activation energy for k 2 and k 3 was always negative, which is physically unrealistic. The solid lines in Fig. 9a provides one such fit, for the case with k 3 50, indicating excellent correspondence with the data. The fitted parameters for this case are: k 1 5 0.9442 exp(27794.1 /T ) and k 2 5 0.0045 exp(907.5 /T ) min 21 , with T having the unit of Kelvin. The negative activation energy for k 2 is clearly evident, invalidating this kinetic model.

491

As an alternative an nth order heterogeneous irreversible reaction model: dg ] 5 k(1 2 g)n dt

(10)

with a Gaussian distribution of activation energies [11,12]: 1 f(E) 5 ]] exp Œ] 2ps

F

(E 2 Eo )2 2 ]]] 2s 2

G

(11)

was also fitted to the data. Such a model would arise if there are various forms of the disorganized carbon or defects, each of which has a different activation energy for incorporation into the lattice of organized carbon. The dashed curves in Fig. 9a depict the fitted results, showing somewhat poorer agreement with the data compared to the reversible reaction model. However, the parameter values are physically more consistent, yielding n515.37, k 0 5 1.52310 5 s 21 , E0 5190.8 kJ mol 21 , s 531.4 kJ mol 21 , and g0 50.222. Here k 0 is a constant frequency factor, and g0 is the initial value of g taken as a fitting parameter for improved fit. The estimated activation energy distribution function for the nth order reaction model with a distribution of energy is depicted in Fig. 9b. The value of the order n has been found to be around 15 in our study. Murty et al. [37] studied the degree of graphitization using an empirical equation, that was recently shown by Salatino et al. [38] to be related to the nth order reaction of heat annealing. The value of n was found to be around 7 in their study. If the nth order reaction model holds, then the large value of n implies that the heat annealing or structural ordering involves multiple carbon atoms simultaneously, which seems reasonable. The mean activation energy of 190.8 kJ mol 21 is in the range reported earlier [19] for a chemically controlled system. However, the inadequate fit, as well as the indication of a reversible step with low activation energy, obtained by superposition, suggests that this model is inappropriate in the temperature range investigated.

Fig. 9. (a) Comparison of experimental fraction organized carbon of Yarrabee coal char, after heat treatment at various temperatures for various times, with that calculated by the reaction models. Solid lines represents results from the first order reversible reaction model. Dashed lines are results from the nth order irreversible reaction model with a Gaussian distribution of activation energy. (b) Activation energy distribution function obtained by the activation energy distribution model.

3.5.3. Diffusion model The failure of the reaction rate models, and the low activation energy of the evolution as obtained by superposition, prompted the consideration of a diffusional kinetics for the structural re-organization process. In the literature two possible mechanisms for the catalytic ordering have been proposed [19]: (a) preferential solution of disordered carbon and precipitation of graphite, or (b) the formation of a moving zone of carbide. In the latter case decomposition of the carbide at the trailing surface of the moving zone can occur to form graphite and free metal, which diffuses along the surface of the carbide particle to react with disordered carbon at the leading interface. The kinetics of the reaction would depend on the amount of carbide, the size of the carbide zone, and the rate of diffusion of carbon through the carbide or of metal over

B. Feng et al. / Carbon 40 (2002) 481 – 496

492

the carbide / graphite interface. Although hypothesised, such a mechanism has never been translated into a model to quantitatively interpret actual graphitization data. In formulating the diffusion model we assume that while the chemical mechanism of Eq. (1) is valid, the rate of formation of g * is controlled by a diffusional process, and that the second step has negligible rate at the heat treatment temperature. On cooling, the metastable g * irreversibly transforms to stable g by gradual or abrupt relaxation of the matrix strain. The precise mechanism of the diffusional process is not postulated a priori, nor based here on the above carbide hypothesis, but is subsequently speculated on based on the parameter values obtained. With these assumptions the measured evolution g(t) will also display a diffusional kinetics, following the model:

S D

≠g˜ De ≠ ≠g˜ ]5] ] rs ] ≠t ≠r r s ≠r

(12)

g˜ (0,r) 5 go , 0 # r # L

(13)

≠g˜ ] 5 0 at r 5 0, t $ 0 ≠r

(14)

˜ g(L,t) 5 ge 5 g e* 1 go , t . 0

(15)

whose solution provides the result for g(t), taken as the ˜ spatial average of g(r,t) at any time, given by: L

(s 1 1) g(t) 5 ]] L s 11

˜ E r g(r,t) dr s

(16)

0

˜ is the local extent of ordering at position r, De is Here g(t) an effective diffusivity, g e* is the equilibrium value of g * , L is the size of the diffusion domain and s is a parameter representing the geometry of this domain. For linear geometry s50, while for cylindrical and spherical geometry s51 and s52, respectively. It is well known, however, that solutions for g(t) to this diffusion problem are insensitive to the value of s if the value of L is suitably chosen based on the volume to surface area ratio of the domain. Accordingly s50 has been chosen here for simplicity, although the graphitized region around and between discrete iron particles may well have a more complex and nonideal geometry. For this case (i.e. s50) we obtain the solution:

Fig. 10. (a) Comparison of experimental fraction organized carbon of Yarrabee coal char, after heat treatment at various temperatures for various times, with that calculated by the diffusion model. (b) Variation of apparent equilibrium constant with heat treatment temperature.

O

8( ge 2 go ) ` 1 ]]]2 g(t) 5 ge 2 ]]] p2 n 50 (2n 1 1)

S

(2n 1 1)2 p 2 De t 3 exp 2 ]]]] 4L 2

D

(17) 2

which was fitted to the data to yield ge and the ratio De /L at each temperature, with go 50.25 (the measured value). Fig. 10a depicts the comparison with the data, showing remarkably good fit for this model, which is considerably better than that of the chemically controlled kinetics depicted in Fig. 9a. Table 2 provides the values of the

Table 2 Fitted parameters of diffusion model Temperature (8C)

De /L 2 (min 21 )

Fraction-ordered carbon, ge

1150 1100 1050 1000 950

0.0037 0.0035 0.0034 0.0034 0.0030

0.4944 0.4576 0.4137 0.3938 0.3642

B. Feng et al. / Carbon 40 (2002) 481 – 496

parameters obtained, showing the fitted final values of g (i.e. ge ) to be only slightly larger than those apparent from Fig. 2. From the extracted values of De /L 2 an apparent activation energy for the diffusion, ED , of 11.8 kJ mol 21 is obtained, as depicted in Fig. 8. This is consistent with that expected for the rate controlling process, while proceeding to an apparent equilibrium, based on the results from superposition shown in Fig. 8. The final value of ge may also be used to extract the equilibrium constant for the apparently reversible first step, following: g *e (T ) ge (T ) 2 go K(T ) 5 ]] 5 ]]] u e (T ) 1 2 ge (T )

(18)

the results for which are depicted in Fig. 10b, yielding an endothermic heat of reaction for this step of DH o 571.7 kJ mol 21 , based on the relation: d ln(K) DH o ]] 5 2 ]] R d(1 /T )

(19)

The irreversible second step may be expected to be highly exothermic, but the present data is not sufficient to explore this second step. The relative success of the diffusion model compared to the chemical rate models, and the micrographic evidence presented here, leads us to conclude that this is phenomenologically more appropriate. The micrographic evidence, based on HRTEM, shows maximum ordering in the vicinity of Fe particles, and less farther away from these. If some form of transport is not involved the influence of iron would be expected to be localized to its surface, and not extend into the surrounding region. Additionally, if this transport were to be relatively rapid the ordering would be uniform and not dependent on position. All of these suggest strong influence of a transport resistance, although its detailed form and mechanism is not obvious. The low activation energy of 11.8 kJ mol 21 for the diffusion is another feature supporting this viewpoint, as such processes are generally characterized by low activation energies except when in the solid state. The value obtained is in the range often found for surface diffusion, and it is instructive to speculate on a mechanism involving surface transport of iron atoms within the crystallite nanospaces. While it is not clear how the iron catalyzes the ordering of the carbon, if the local value of g is related to the concentration of iron, then the control by the diffusional resistance of iron would explain the observed kinetics. Indeed, the activation energy of 11.8 kJ mol 21 is only marginally less than the heat of melting of iron (or the energy needed to render it mobile), of about 14.9 kJ mol 21 , supporting the possibility of surface transport of iron. From the fitted values of De /L 2 , the value of De is estimated to be around 2310 216 cm 2 s 21 , assuming the size of the diffusion domain, L, to be about 20 nm which appears reasonable based on the micrographs in Fig. 4. This value of De is far too small for vapor phase transport,

493

but is in the appropriate region for configurational diffusion in confined nanospaces such as in molecular sieve materials, or solid-state diffusion [39]. The precise nature of how iron influences or catalyzes the ordering is at this time not obvious. The carbide hypothesis of Fischbach [19] is plausible, but cannot be presently confirmed. If this is the mode of catalysis, then the effective diffusivity relates to the movement of the carbide zone, starting from the iron surface. In our XRD studies the presence of iron carbide could only be confirmed for the highest temperature used (11508C) and further detailed studies are needed to test this mechanism. Although catalytic graphitization has been reported and utilized in the graphitization process, its importance during gasification has never been revealed. There are almost always various kinds of minerals in coal, even in the high rank coals such as the one studied here, many of which can be very active catalysts for graphitization. Also, O 2 and CO 2 atmospheres can enhance graphitization as reported in literature [33,34], though this aspect is not explored here. Catalytic ordering can, at least partially, explain the fact that low rank coals are more sensitive to heat treatment than high rank coals. It is, therefore, necessary to assess the importance of in situ ordering during gasification for various coals.

3.6. Relation between XRD parameters and carbon structure There is a discrepancy in the value of Lc between the Scherrer equation and by the Shi et al. XRD theory [22]. ˚ so The Lc value by the Scherrer equation is around 8 A, that the stack number is around 2–3 (assuming interplanar ˚ However, a layer number of around 19 spacing of 3.4 A). was found from the program of Shi et al. [22]. Davis et al. [10] also found that the value of La by TEM is four times larger than that by XRD using the Scherrer equation. While the Scherrer equation has been used frequently to obtain the crystallite size of carbon in the past, its accuracy for the highly disordered carbon is doubtful. It is pointed out that the model of Shi et al. [22] produces the whole size of the crystallite planes while the Scherrer equation gives the more organized part of the planes [26]. Therefore, the crystallite size by Scherrer’s equation is inevitably smaller except for the well ordered carbon. This underestimation of the crystallite size is also suggested by a recent quantum mechanical study [40] of aromatic hydrocarbons in carbonaceous materials. The number of the crystallite layers in a coal, or the stacking height Lc , depends on its age and / or its carbon content. Hirsch [41] has shown that high rank coals with a carbon content higher than 94% generally have more than eight layers in the crystallites. Therefore, the number of layer of 2–3 by Scherrer’s equation is unlikely to be correct for the carbon studied. This can also be confirmed from the surface area point of view. Assum-

B. Feng et al. / Carbon 40 (2002) 481 – 496

494

rings of carbon atoms on a layer, i.e. m 5 La / 2h, where h is the radial distance between successive rings (0.15 nm). ´m 0 is the microporosity of the grains, obtained from:

´m 0 5Vg rs /(1 1Vg rs )

(21)

in which Vg and rs are the micorpore volume and solid density, obtained from argon adsorption and helium pyconometry, respectively. The number of layers, H, is related to the value of Lc through the relation H 5 Lc /d, where d (50.34 nm) is the interlayer spacing. Eq. (20) assumes a configuration of circular rings of carbon atoms on a plane, with the total number of atoms in a crystallite layer depending on its size La [42], following:

Œ]3 N t 5 ]p m(m 1 1) 2

The variation of the total number of atoms in a layer, Nt , with size of the crystallite layer, La , is shown in Fig. 11, using experimental result drawn from Diamond [43]. It is clear that the assumption of circular rings of atoms is acceptable. The surface areas of the heat-treated coal chars and two commercial activated carbon were measured and compared with those calculated from Eq. (20) using the values of La and Lc from Scherrer equation and the Shi et al. model. The results are listed in Table 3. The surface area calculated form Eq. (20) using the values of La and Lc obtained from the Scherrer equation is much higher than the measured surface area, while the one calculated using the values of La and Lc obtained from the Shi et al. model is much closer. Therefore, it would appear that the Scherrer equation underestimates the crystallite size significantly when it is used to analyze disordered carbon. In utilizing Shi et al.’s model and program we chose the

Fig. 11. Relationship between the number of atoms on a crystallite plane with the size of the plane. Points are experimental data of Diamond [43]. Curve is based on Eq. (22) from Bhatia [42].

ing crystallites to be randomly aggregating in grains, Bhatia [42] has derived an equation for the surface area of crystallitic carbon, in the form: 5912[H(m 1 2.33) 1 0.4412m 2 ] ´m 0 ln(1 /´m 0 ) Sg 5 ]]]]]]]]]]]] m(m 1 1) H(1 2 ´m 0 )

(22)

(20)

in which H is the number of layers, m is the number of

Table 3 Structural parameters of carbons Method

Parameter

y2hA Char a

BPL-AC b

F400-AC c

Experimental data

Vg (cm 3 g 21 )d rs (g cm 23 )e ´m 0 Smeasured (m 2 g 21 )f

0.12815 2.1 0.21205 427.5

0.4660 2.2 0.50620 889.0

0.42956 2.2 0.48588 884.7

Scherrer equation

La (nm) Lc (nm)g Scalculated (m 2 g 21 )h

g

1.7 0.8 795.6

1.7 0.8 1366.1

1.7 0.8 1308.2

Shi et al. model [22]

La (nm) Lc (nm)i Scalculated (m 2 g 21 )j

i

2.0 5.5 461.2

1.9 3.2 840.9

2.1 2.0 894.0

a

y2mA heat-treated at 11508C for 2 h and gasified in oxygen at 4008C to 8% conversion. b BPL activated carbon from Calgon Carbon Co. F-400 activated carbon from Calgon Carbon Co. d Micropore volume by argon adsorption. e Solid density by helium pyconometry. f Surface area by argon adsorption and density function theory. g Values by the Scherrer equation from the XRD patterns. h Surface area by Eq. (20) using La and Lc values estimated by Scherrer’s equation. i Values by Shi et al. model. j Surface area estimated by Eq. (20) using the La and Lc values by Shi et al.’s model. c

B. Feng et al. / Carbon 40 (2002) 481 – 496

parameter values providing the global minimum based on different starting guesses. Other local minima with slightly larger fitting error existed, however, it was found that even though the absolute values of La and Lc could be different at these minima, the behavior of La , Lc and g did not vary. Thus the values of La and Lc increase only slightly with the increase of heat treatment temperature and the heating time, and g increases fast initially and then slowly with increase of the heating time. Furthermore, the values of the fraction of ordered carbon, g, were essentially the same at the different minima, and the conclusion that catalytic ordering occurs actively during heat treatment and decreases the reactivity significantly is unaffected.

4. Conclusions The structure of an Australian coal during heat treatment in a temperature range of 950–11508C was studied using the techniques of XRD, HRTEM and electrical resistivity. The variation of crystallite size and the crystallite order of the coal char during heat treatment have been investigated. The following conclusions are drawn: 1. The crystallite size of carbon during heat treatment in the temperature range studied does not vary strongly with temperature and heating time. However, the crystallites are more ordered after heat treatment at high temperature and for longer time. The perfecting process seems to result in a slight rise in the crystallite width. 2. Catalytic ordering occurs actively during heat treatment with iron being an effective catalyst. The carbon near the iron particle shows graphite-like structure while the carbon away from the iron is disordered. This results in increase of the fraction of organized carbon in char. 3. The fraction of organized carbon can be correlated with reactivity of the char. It suggests that thermal deactivation is caused by the structural ordering during heat treatment, which is enhanced by the catalysts in char. 4. In the temperature range studied the fraction of organized carbon approaches an apparent equilibrium limit that increases with temperature, with an endothermic heat of this process of 71.7 kJ mol 21 . 5. In the temperature range studied the kinetics of ordering is controlled by a diffusion process with a small activation energy of 11.8 kJ mol 21 . It is speculated that the mechanism may involve surface transport of iron in the crystallite nanospaces, but this could not be confirmed in the present study.

Acknowledgements The financial support of the Australian Research Council (ARC) under the Large Research Grant Scheme is gratefully acknowledged

495

References [1] Jenkins RG, Nandi SP, Walker Jr. PL. Fuel 1973;52(October):288. [2] Radovic LR, Walker Jr. PL, Jenkins RG. Fuel 1983;62(July):849. [3] Cai HY, Guell AJ, Chatzakis IN, Lim JY, Dugwell DR, Kandiyoti R. Fuel 1996;75(1):15. [4] Shim HS, Hurt RH. Energy Fuels 2000;14:340. [5] Zolin A, Jensen A, Dam-Johansen K. Kinetic analysis of char thermal deactivation. In: Proceedings of the 28th international symposium on combustion, The Combustion Institute, 2000. [6] Russell NV, Gibbins JR, Man CK, Williamson J. Energy Fuels 2000;14:883. [7] Hurt RH, Gibbins JR. Fuel 1995;74(4):471. [8] Senneca O, Russo P, Salatino P, Masi S. Carbon 1997;35(1):141. [9] Beeley T, Crelling J, Gibbins J, Hurt R, Lunden M, Man C, Williamson J, Yang N. Transient high-temperature thermal deactivation of monomaceral-rich coal chars. In: Proceedings of the 26th international symposium on combustion, The Combustion Institute, 1996, p. 3103. [10] Davis KA, Hurt RH, Yang NYC, Headley TJ. Combust Flame 1995;100:31. [11] Suuberg EM, Wojtowicz M, Calo JM. Carbon 1989;27:431. [12] Suuberg EM. In: Lahaye J, Ehrburger P, editors, Fundamental issues in control of carbon gasification reactivity, Dordrecht: Kluwer, 1991, p. 269. [13] Oberlin A, Oberlin MJ. J Microsc 1983;132:353. [14] Oberlin A. Carbon 1984;22:521. [15] Rouzaud JN, Oberlin A. Carbon 1989;27(4):517. [16] Rouzaud JN. Fuel Process Technol 1990;24:55. [17] Emmerich FG. Carbon 1995;33(12):1709. [18] Grumer T, Zerda TW, Gerspacher M. Carbon 1994;32(7):1377. [19] Fischbach DB. In: Walker Jr. PL, editor, Chemistry and physics of carbon, vol. 7, New York: Marcel Dekker, 1971, p. 1. [20] Pacault A. In: Walker Jr. PL, editor, Chemistry and physics of carbon, vol. 7, New York: Marcel Dekker, 1971, p. 107. [21] Nagle J, Strickland-Constable RF, editors, Proceedings of the fifth carbon conference, vol. 1, London: Pergamon Press, 1962, p. 154. [22] Shi H, Reimers JN, Dahn JR. J Appl Crystallogr 1993;26:827. [23] Feng B, Bhatia SK. Energy Fuels 2000;14:297. [24] Dahn JR, Xing W, Gao Y. Carbon 1997;35(6):825. [25] Babu VS, Seehra MS. Carbon 1996;34(10):1259. [26] Shi, H., Disordered carbons and battery applications. Department of Physics, Simon Fraser University, 155 pp. [27] Franklin RE. Acta Crystallogr 1951;4:253. [28] Ruland W. In: Walker Jr. PL, editor, Chemistry and physics of carbon, vol. 4, New York: Marcel Dekker, 1968, p. 1. [29] Shim H-S, Hurt RH, Yang NYC. Carbon 2000;38:29. [30] Sharma A, Kyotani T, Tomita A. Fuel 1999;78(10):1203. [31] Albert P. J Chem Phys 1969;4:171. [32] Wang W, Thomas KM, Poultney RM, Wilmers RR. Carbon 1995;33:1525. [33] Noda T, Inagaki M. Nature 1962;196:772. [34] Noda T, Inagaki M. Carbon 1964;2:127.

496

B. Feng et al. / Carbon 40 (2002) 481 – 496

[35] Livneh T, Bar-Ziv E, Senneca O, Salatino P. Combust Sci Technol 2000;153:65. [36] Rao CNR, Yoganarasimhan SR, Lewis MP. Can J Chem 1960;38:2359. [37] Murty HN, Biedermann DL, Heintz EA. Carbon 1969;7:667. [38] Salatino P, Senneca O, Masi S. Energy Fuels 1999;13(6):1154.

[39] Froment GF, Bischoff KB. Chemical reactor analysis and design, New York: Wiley, 1990. [40] Marzec A. Carbon 2000;38:1863. [41] Hirsch PB. Proc R Soc (Lond) A 1954;226:143. [42] Bhatia SK. AIChE J 1998;44(11):2478. [43] Diamond R. Acta Crystallogr 1957;10:359.